Remote visual analysis of large turbulence databases at multiple scales

The remote analysis and visualization of raw large turbulence datasets is challenging. Current accurate direct numerical simulations (DNS) of turbulent flows generate datasets with billions of points per time-step and several thousand time-steps per simulation. Until recently, the analysis and visua...

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Published in:Journal of parallel and distributed computing Vol. 120; pp. 115 - 126
Main Authors: Pulido, Jesus, Livescu, Daniel, Kanov, Kalin, Burns, Randal, Canada, Curtis, Ahrens, James, Hamann, Bernd
Format: Journal Article
Language:English
Published: United States Elsevier Inc 01-10-2018
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Abstract The remote analysis and visualization of raw large turbulence datasets is challenging. Current accurate direct numerical simulations (DNS) of turbulent flows generate datasets with billions of points per time-step and several thousand time-steps per simulation. Until recently, the analysis and visualization of such datasets was restricted to scientists with access to large supercomputers. The public Johns Hopkins Turbulence database simplifies access to multi-terabyte turbulence datasets and facilitates the computation of statistics and extraction of features through the use of commodity hardware. We present a framework designed around wavelet-based compression for high-speed visualization of large datasets and methods supporting multi-resolution analysis of turbulence. By integrating common technologies, this framework enables remote access to tools available on supercomputers and over 230 terabytes of DNS data over the Web. The database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction. •Data-level wavelet compression reduces bandwidth, memory and compute footprint.•Latency is improved between database components and multi-user support is introduced.•Remote visualization is enabled on a large database cluster using commodity hardware.•New wavelet analysis tools are demonstrated for existing turbulence data cluster.
AbstractList The remote analysis and visualization of raw large turbulence datasets is challenging. Current accurate direct numerical simulations (DNS) of turbulent flows generate datasets with billions of points per time-step and several thousand time-steps per simulation. Until recently, the analysis and visualization of such datasets was restricted to scientists with access to large supercomputers. The public Johns Hopkins Turbulence database simplifies access to multi-terabyte turbulence datasets and facilitates the computation of statistics and extraction of features through the use of commodity hardware. In this paper, we present a framework designed around wavelet-based compression for high-speed visualization of large datasets and methods supporting multi-resolution analysis of turbulence. By integrating common technologies, this framework enables remote access to tools available on supercomputers and over 230 terabytes of DNS data over the Web. Finally, the database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction.
The remote analysis and visualization of raw large turbulence datasets is challenging. Current accurate direct numerical simulations (DNS) of turbulent flows generate datasets with billions of points per time-step and several thousand time-steps per simulation. Until recently, the analysis and visualization of such datasets was restricted to scientists with access to large supercomputers. The public Johns Hopkins Turbulence database simplifies access to multi-terabyte turbulence datasets and facilitates the computation of statistics and extraction of features through the use of commodity hardware. We present a framework designed around wavelet-based compression for high-speed visualization of large datasets and methods supporting multi-resolution analysis of turbulence. By integrating common technologies, this framework enables remote access to tools available on supercomputers and over 230 terabytes of DNS data over the Web. The database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction. •Data-level wavelet compression reduces bandwidth, memory and compute footprint.•Latency is improved between database components and multi-user support is introduced.•Remote visualization is enabled on a large database cluster using commodity hardware.•New wavelet analysis tools are demonstrated for existing turbulence data cluster.
Author Hamann, Bernd
Canada, Curtis
Livescu, Daniel
Burns, Randal
Kanov, Kalin
Ahrens, James
Pulido, Jesus
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Turbulence
Databases
Distributed systems
Data reduction
Remote visualization
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Snippet The remote analysis and visualization of raw large turbulence datasets is challenging. Current accurate direct numerical simulations (DNS) of turbulent flows...
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SubjectTerms Computer Science
Data Reduction
Distributed Systems
MATHEMATICS AND COMPUTING
Remote Visualization
Turbulence
Wavelets
Title Remote visual analysis of large turbulence databases at multiple scales
URI https://dx.doi.org/10.1016/j.jpdc.2018.05.011
https://www.osti.gov/servlets/purl/1457276
Volume 120
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